Overview

Dataset statistics

Number of variables24
Number of observations1677
Missing cells13
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory314.6 KiB
Average record size in memory192.1 B

Variable types

Categorical6
Numeric18

Alerts

YearsSinceLastPromotion is highly overall correlated with YearsAtCompanyHigh correlation
YearsAtCompany is highly overall correlated with YearsSinceLastPromotion and 5 other fieldsHigh correlation
CurrManagerTotal is highly overall correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsInJobs is highly overall correlated with YearsAtCompany and 4 other fieldsHigh correlation
NumCompaniesWorked is highly overall correlated with YearsInJobsHigh correlation
Age is highly overall correlated with TotalWorkingYearsHigh correlation
MonthlyIncome is highly overall correlated with TotalWorkingYearsHigh correlation
TotalWorkingYears is highly overall correlated with YearsAtCompany and 5 other fieldsHigh correlation
YearsInCurrentRole is highly overall correlated with YearsAtCompany and 4 other fieldsHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompany and 4 other fieldsHigh correlation
TrainingTimesLastYear has 50 (3.0%) zerosZeros
YearsSinceLastPromotion has 726 (43.3%) zerosZeros
YearsAtCompany has 54 (3.2%) zerosZeros
CurrManagerTotal has 285 (17.0%) zerosZeros
YearsInCurrentRole has 307 (18.3%) zerosZeros
YearsWithCurrManager has 298 (17.8%) zerosZeros

Reproduction

Analysis started2023-03-06 20:39:42.356583
Analysis finished2023-03-06 20:41:42.437584
Duration2 minutes and 0.08 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
4
537 
3
496 
2
345 
1
299 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4 537
32.0%
3 496
29.6%
2 345
20.6%
1 299
17.8%

Length

2023-03-06T21:41:42.602925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:41:42.860457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
4 537
32.0%
3 496
29.6%
2 345
20.6%
1 299
17.8%

Most occurring characters

ValueCountFrequency (%)
4 537
32.0%
3 496
29.6%
2 345
20.6%
1 299
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 537
32.0%
3 496
29.6%
2 345
20.6%
1 299
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 537
32.0%
3 496
29.6%
2 345
20.6%
1 299
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 537
32.0%
3 496
29.6%
2 345
20.6%
1 299
17.8%

StockOptionLevel
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
1
745 
0
732 
2
135 
3
 
65

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 745
44.4%
0 732
43.6%
2 135
 
8.1%
3 65
 
3.9%

Length

2023-03-06T21:41:43.101194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:41:43.396656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 745
44.4%
0 732
43.6%
2 135
 
8.1%
3 65
 
3.9%

Most occurring characters

ValueCountFrequency (%)
1 745
44.4%
0 732
43.6%
2 135
 
8.1%
3 65
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 745
44.4%
0 732
43.6%
2 135
 
8.1%
3 65
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 745
44.4%
0 732
43.6%
2 135
 
8.1%
3 65
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 745
44.4%
0 732
43.6%
2 135
 
8.1%
3 65
 
3.9%

Education
Real number (ℝ)

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9379845
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:43.618488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum15
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0390784
Coefficient of variation (CV)0.3536705
Kurtosis10.019495
Mean2.9379845
Median Absolute Deviation (MAD)1
Skewness0.59693672
Sum4927
Variance1.079684
MonotonicityNot monotonic
2023-03-06T21:41:43.829986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 682
40.7%
4 464
27.7%
2 304
18.1%
1 182
 
10.9%
5 44
 
2.6%
15 1
 
0.1%
ValueCountFrequency (%)
1 182
 
10.9%
2 304
18.1%
3 682
40.7%
4 464
27.7%
5 44
 
2.6%
15 1
 
0.1%
ValueCountFrequency (%)
15 1
 
0.1%
5 44
 
2.6%
4 464
27.7%
3 682
40.7%
2 304
18.1%
1 182
 
10.9%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
3
1107 
2
358 
4
139 
1
 
73

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1107
66.0%
2 358
 
21.3%
4 139
 
8.3%
1 73
 
4.4%

Length

2023-03-06T21:41:44.069501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:41:44.342741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1107
66.0%
2 358
 
21.3%
4 139
 
8.3%
1 73
 
4.4%

Most occurring characters

ValueCountFrequency (%)
3 1107
66.0%
2 358
 
21.3%
4 139
 
8.3%
1 73
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1107
66.0%
2 358
 
21.3%
4 139
 
8.3%
1 73
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1107
66.0%
2 358
 
21.3%
4 139
 
8.3%
1 73
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1107
66.0%
2 358
 
21.3%
4 139
 
8.3%
1 73
 
4.4%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
4
561 
3
516 
1
310 
2
290 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row4
4th row1
5th row1

Common Values

ValueCountFrequency (%)
4 561
33.5%
3 516
30.8%
1 310
18.5%
2 290
17.3%

Length

2023-03-06T21:41:44.634487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:41:44.901551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
4 561
33.5%
3 516
30.8%
1 310
18.5%
2 290
17.3%

Most occurring characters

ValueCountFrequency (%)
4 561
33.5%
3 516
30.8%
1 310
18.5%
2 290
17.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 561
33.5%
3 516
30.8%
1 310
18.5%
2 290
17.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 561
33.5%
3 516
30.8%
1 310
18.5%
2 290
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 561
33.5%
3 516
30.8%
1 310
18.5%
2 290
17.3%
Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
3
523 
4
518 
2
337 
1
299 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row4
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 523
31.2%
4 518
30.9%
2 337
20.1%
1 299
17.8%

Length

2023-03-06T21:41:45.170158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:41:45.431924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 523
31.2%
4 518
30.9%
2 337
20.1%
1 299
17.8%

Most occurring characters

ValueCountFrequency (%)
3 523
31.2%
4 518
30.9%
2 337
20.1%
1 299
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 523
31.2%
4 518
30.9%
2 337
20.1%
1 299
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 523
31.2%
4 518
30.9%
2 337
20.1%
1 299
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 523
31.2%
4 518
30.9%
2 337
20.1%
1 299
17.8%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
3
1089 
2
385 
4
135 
1
 
68

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1677
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 1089
64.9%
2 385
 
23.0%
4 135
 
8.1%
1 68
 
4.1%

Length

2023-03-06T21:41:45.693993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-06T21:41:45.978411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1089
64.9%
2 385
 
23.0%
4 135
 
8.1%
1 68
 
4.1%

Most occurring characters

ValueCountFrequency (%)
3 1089
64.9%
2 385
 
23.0%
4 135
 
8.1%
1 68
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1089
64.9%
2 385
 
23.0%
4 135
 
8.1%
1 68
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1089
64.9%
2 385
 
23.0%
4 135
 
8.1%
1 68
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1089
64.9%
2 385
 
23.0%
4 135
 
8.1%
1 68
 
4.1%

TrainingTimesLastYear
Real number (ℝ)

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7328563
Minimum0
Maximum6
Zeros50
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:46.185320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1452714
Coefficient of variation (CV)0.41907487
Kurtosis1.0078225
Mean2.7328563
Median Absolute Deviation (MAD)1
Skewness0.57095907
Sum4583
Variance1.3116466
MonotonicityNot monotonic
2023-03-06T21:41:46.402289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 655
39.1%
3 626
37.3%
5 122
 
7.3%
4 122
 
7.3%
1 63
 
3.8%
0 50
 
3.0%
6 39
 
2.3%
ValueCountFrequency (%)
0 50
 
3.0%
1 63
 
3.8%
2 655
39.1%
3 626
37.3%
4 122
 
7.3%
5 122
 
7.3%
6 39
 
2.3%
ValueCountFrequency (%)
6 39
 
2.3%
5 122
 
7.3%
4 122
 
7.3%
3 626
37.3%
2 655
39.1%
1 63
 
3.8%
0 50
 
3.0%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6839595
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:46.672503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q312
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.8261426
Coefficient of variation (CV)0.90121823
Kurtosis0.10082791
Mean8.6839595
Median Absolute Deviation (MAD)5
Skewness1.0715877
Sum14563
Variance61.248507
MonotonicityNot monotonic
2023-03-06T21:41:46.972582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 278
16.6%
1 237
14.1%
8 104
 
6.2%
9 101
 
6.0%
10 99
 
5.9%
3 95
 
5.7%
7 91
 
5.4%
6 78
 
4.7%
5 74
 
4.4%
4 64
 
3.8%
Other values (19) 456
27.2%
ValueCountFrequency (%)
1 237
14.1%
2 278
16.6%
3 95
 
5.7%
4 64
 
3.8%
5 74
 
4.4%
6 78
 
4.7%
7 91
 
5.4%
8 104
 
6.2%
9 101
 
6.0%
10 99
 
5.9%
ValueCountFrequency (%)
29 27
1.6%
28 26
1.6%
27 9
 
0.5%
26 24
1.4%
25 26
1.6%
24 28
1.7%
23 24
1.4%
22 16
1.0%
21 18
1.1%
20 25
1.5%

YearsSinceLastPromotion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9785331
Minimum0
Maximum15
Zeros726
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:47.243382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile8.2
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0457164
Coefficient of variation (CV)1.5393811
Kurtosis4.1578175
Mean1.9785331
Median Absolute Deviation (MAD)1
Skewness2.0807537
Sum3318
Variance9.2763885
MonotonicityNot monotonic
2023-03-06T21:41:47.478844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 726
43.3%
1 407
24.3%
2 148
 
8.8%
7 89
 
5.3%
4 78
 
4.7%
3 53
 
3.2%
5 41
 
2.4%
6 36
 
2.1%
11 31
 
1.8%
8 15
 
0.9%
Other values (6) 53
 
3.2%
ValueCountFrequency (%)
0 726
43.3%
1 407
24.3%
2 148
 
8.8%
3 53
 
3.2%
4 78
 
4.7%
5 41
 
2.4%
6 36
 
2.1%
7 89
 
5.3%
8 15
 
0.9%
9 15
 
0.9%
ValueCountFrequency (%)
15 12
 
0.7%
14 6
 
0.4%
13 8
 
0.5%
12 8
 
0.5%
11 31
 
1.8%
10 4
 
0.2%
9 15
 
0.9%
8 15
 
0.9%
7 89
5.3%
6 36
2.1%

YearsAtCompany
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8067979
Minimum0
Maximum41
Zeros54
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:47.791448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum41
Range41
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.8832821
Coefficient of variation (CV)0.8643245
Kurtosis3.9297715
Mean6.8067979
Median Absolute Deviation (MAD)3
Skewness1.7382885
Sum11415
Variance34.613009
MonotonicityNot monotonic
2023-03-06T21:41:48.101545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
5 228
13.6%
1 214
12.8%
3 176
10.5%
10 138
8.2%
4 121
 
7.2%
2 108
 
6.4%
7 103
 
6.1%
9 99
 
5.9%
8 91
 
5.4%
6 87
 
5.2%
Other values (24) 312
18.6%
ValueCountFrequency (%)
0 54
 
3.2%
1 214
12.8%
2 108
6.4%
3 176
10.5%
4 121
7.2%
5 228
13.6%
6 87
 
5.2%
7 103
6.1%
8 91
 
5.4%
9 99
5.9%
ValueCountFrequency (%)
41 1
 
0.1%
37 1
 
0.1%
34 2
 
0.1%
33 5
0.3%
31 4
0.2%
30 2
 
0.1%
29 3
0.2%
27 2
 
0.1%
26 1
 
0.1%
25 5
0.3%

CurrManagerTotal
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct175
Distinct (%)10.5%
Missing13
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean0.41373267
Minimum0
Maximum1.6
Zeros285
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:48.422439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.16666667
median0.4
Q30.66666667
95-th percentile0.88888889
Maximum1.6
Range1.6
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.29710447
Coefficient of variation (CV)0.71810734
Kurtosis-1.0034959
Mean0.41373267
Median Absolute Deviation (MAD)0.26666667
Skewness0.16902311
Sum688.45117
Variance0.088271067
MonotonicityNot monotonic
2023-03-06T21:41:48.742442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 285
 
17.0%
0.5 120
 
7.2%
0.3333333333 88
 
5.2%
0.6666666667 83
 
4.9%
0.8 80
 
4.8%
0.7 73
 
4.4%
0.25 59
 
3.5%
1 47
 
2.8%
0.4 45
 
2.7%
0.2 45
 
2.7%
Other values (165) 739
44.1%
ValueCountFrequency (%)
0 285
17.0%
0.02941176471 1
 
0.1%
0.03571428571 1
 
0.1%
0.03703703704 1
 
0.1%
0.04 1
 
0.1%
0.05 2
 
0.1%
0.05263157895 3
 
0.2%
0.05882352941 3
 
0.2%
0.0625 3
 
0.2%
0.06666666667 6
 
0.4%
ValueCountFrequency (%)
1.6 1
 
0.1%
1 47
2.8%
0.9375 1
 
0.1%
0.9230769231 2
 
0.1%
0.9166666667 1
 
0.1%
0.9090909091 1
 
0.1%
0.9 20
1.2%
0.8888888889 12
 
0.7%
0.8823529412 1
 
0.1%
0.875 31
1.8%

YearsInJobs
Real number (ℝ)

Distinct154
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0578241
Minimum0
Maximum41
Zeros13
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:49.073489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.1428571
median5
Q39
95-th percentile16
Maximum41
Range41
Interquartile range (IQR)6.8571429

Descriptive statistics

Standard deviation5.3982349
Coefficient of variation (CV)0.8911178
Kurtosis5.9949779
Mean6.0578241
Median Absolute Deviation (MAD)3
Skewness2.0160952
Sum10158.971
Variance29.14094
MonotonicityNot monotonic
2023-03-06T21:41:49.402499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 162
 
9.7%
1 144
 
8.6%
6 119
 
7.1%
5 111
 
6.6%
4 90
 
5.4%
2 71
 
4.2%
3 70
 
4.2%
9 68
 
4.1%
8 49
 
2.9%
7 43
 
2.6%
Other values (144) 750
44.7%
ValueCountFrequency (%)
0 13
0.8%
0.2222222222 1
 
0.1%
0.3333333333 1
 
0.1%
0.375 1
 
0.1%
0.4444444444 2
 
0.1%
0.5 11
0.7%
0.5714285714 2
 
0.1%
0.625 1
 
0.1%
0.6666666667 16
1.0%
0.7142857143 4
 
0.2%
ValueCountFrequency (%)
41 1
 
0.1%
38 1
 
0.1%
34 4
0.2%
33 3
0.2%
31 3
0.2%
29 1
 
0.1%
28 2
 
0.1%
26 3
0.2%
25 1
 
0.1%
24 5
0.3%

NumCompaniesWorked
Real number (ℝ)

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7257007
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:49.635890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3456941
Coefficient of variation (CV)0.86058388
Kurtosis0.49636451
Mean2.7257007
Median Absolute Deviation (MAD)0
Skewness1.2754566
Sum4571
Variance5.5022806
MonotonicityNot monotonic
2023-03-06T21:41:49.817043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 859
51.2%
2 169
 
10.1%
3 166
 
9.9%
4 161
 
9.6%
7 88
 
5.2%
6 68
 
4.1%
9 63
 
3.8%
5 61
 
3.6%
8 42
 
2.5%
ValueCountFrequency (%)
1 859
51.2%
2 169
 
10.1%
3 166
 
9.9%
4 161
 
9.6%
5 61
 
3.6%
6 68
 
4.1%
7 88
 
5.2%
8 42
 
2.5%
9 63
 
3.8%
ValueCountFrequency (%)
9 63
 
3.8%
8 42
 
2.5%
7 88
 
5.2%
6 68
 
4.1%
5 61
 
3.6%
4 161
 
9.6%
3 166
 
9.9%
2 169
 
10.1%
1 859
51.2%

TotalSatisfaction
Real number (ℝ)

Distinct13
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7708706
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:50.013813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12.5
median2.75
Q33
95-th percentile3.5
Maximum4
Range3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.49370714
Coefficient of variation (CV)0.17817762
Kurtosis-0.029500778
Mean2.7708706
Median Absolute Deviation (MAD)0.25
Skewness-0.2455635
Sum4646.75
Variance0.24374674
MonotonicityNot monotonic
2023-03-06T21:41:50.253882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2.75 356
21.2%
3 303
18.1%
2.5 272
16.2%
3.25 219
13.1%
2.25 175
10.4%
3.5 131
 
7.8%
2 101
 
6.0%
3.75 49
 
2.9%
1.75 38
 
2.3%
1.5 21
 
1.3%
Other values (3) 12
 
0.7%
ValueCountFrequency (%)
1 2
 
0.1%
1.25 4
 
0.2%
1.5 21
 
1.3%
1.75 38
 
2.3%
2 101
 
6.0%
2.25 175
10.4%
2.5 272
16.2%
2.75 356
21.2%
3 303
18.1%
3.25 219
13.1%
ValueCountFrequency (%)
4 6
 
0.4%
3.75 49
 
2.9%
3.5 131
 
7.8%
3.25 219
13.1%
3 303
18.1%
2.75 356
21.2%
2.5 272
16.2%
2.25 175
10.4%
2 101
 
6.0%
1.75 38
 
2.3%

Age
Real number (ℝ)

Distinct43
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.036971
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:50.555788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median35
Q341
95-th percentile52
Maximum60
Range42
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.5071116
Coefficient of variation (CV)0.23606622
Kurtosis-0.13284552
Mean36.036971
Median Absolute Deviation (MAD)6
Skewness0.45409276
Sum60434
Variance72.370947
MonotonicityNot monotonic
2023-03-06T21:41:50.865551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
36 101
 
6.0%
29 96
 
5.7%
34 95
 
5.7%
31 90
 
5.4%
35 89
 
5.3%
38 88
 
5.2%
40 79
 
4.7%
27 67
 
4.0%
30 66
 
3.9%
28 62
 
3.7%
Other values (33) 844
50.3%
ValueCountFrequency (%)
18 12
 
0.7%
19 13
 
0.8%
20 6
 
0.4%
21 16
 
1.0%
22 14
 
0.8%
23 20
 
1.2%
24 23
 
1.4%
25 36
2.1%
26 45
2.7%
27 67
4.0%
ValueCountFrequency (%)
60 3
 
0.2%
59 10
0.6%
58 9
0.5%
57 4
 
0.2%
56 10
0.6%
55 17
1.0%
54 7
 
0.4%
53 15
0.9%
52 18
1.1%
51 14
0.8%

DailyRate
Real number (ℝ)

Distinct625
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean892.74955
Minimum107
Maximum3921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:51.222791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum107
5-th percentile277
Q1589
median890
Q31223
95-th percentile1443
Maximum3921
Range3814
Interquartile range (IQR)634

Descriptive statistics

Standard deviation374.49626
Coefficient of variation (CV)0.41948636
Kurtosis1.3832517
Mean892.74955
Median Absolute Deviation (MAD)316
Skewness0.16161078
Sum1497141
Variance140247.45
MonotonicityNot monotonic
2023-03-06T21:41:51.612612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1082 16
 
1.0%
775 12
 
0.7%
465 11
 
0.7%
855 11
 
0.7%
1329 10
 
0.6%
1157 10
 
0.6%
806 10
 
0.6%
827 10
 
0.6%
989 9
 
0.5%
658 9
 
0.5%
Other values (615) 1569
93.6%
ValueCountFrequency (%)
107 1
 
0.1%
111 1
 
0.1%
115 2
 
0.1%
116 1
 
0.1%
117 5
0.3%
118 1
 
0.1%
119 3
0.2%
124 2
 
0.1%
130 4
0.2%
135 1
 
0.1%
ValueCountFrequency (%)
3921 1
 
0.1%
1499 2
 
0.1%
1498 1
 
0.1%
1495 4
0.2%
1492 1
 
0.1%
1490 4
0.2%
1489 1
 
0.1%
1488 1
 
0.1%
1485 5
0.3%
1482 1
 
0.1%

HourlyRate
Real number (ℝ)

Distinct71
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.79845
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:52.013609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile36
Q151
median69
Q384
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)33

Descriptive statistics

Standard deviation19.435928
Coefficient of variation (CV)0.28667216
Kurtosis-1.1356881
Mean67.79845
Median Absolute Deviation (MAD)16
Skewness-0.12030233
Sum113698
Variance377.7553
MonotonicityNot monotonic
2023-03-06T21:41:52.375058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 41
 
2.4%
84 41
 
2.4%
96 39
 
2.3%
66 39
 
2.3%
78 39
 
2.3%
72 37
 
2.2%
82 37
 
2.2%
41 35
 
2.1%
95 35
 
2.1%
62 35
 
2.1%
Other values (61) 1299
77.5%
ValueCountFrequency (%)
30 13
0.8%
31 5
 
0.3%
32 23
1.4%
33 13
0.8%
34 6
 
0.4%
35 16
1.0%
36 14
0.8%
37 16
1.0%
38 5
 
0.3%
39 12
0.7%
ValueCountFrequency (%)
100 23
1.4%
99 14
 
0.8%
98 32
1.9%
97 26
1.6%
96 39
2.3%
95 35
2.1%
94 21
1.3%
93 18
1.1%
92 29
1.7%
91 24
1.4%

MonthlyIncome
Real number (ℝ)

Distinct895
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6196.0495
Minimum1010
Maximum19973
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:52.732453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1010
5-th percentile2096
Q12875
median4834
Q37403
95-th percentile17610.2
Maximum19973
Range18963
Interquartile range (IQR)4528

Descriptive statistics

Standard deviation4520.0508
Coefficient of variation (CV)0.72950527
Kurtosis1.6846948
Mean6196.0495
Median Absolute Deviation (MAD)2013
Skewness1.5514099
Sum10390775
Variance20430859
MonotonicityNot monotonic
2023-03-06T21:41:52.887346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2911 9
 
0.5%
2559 9
 
0.5%
6811 8
 
0.5%
5228 8
 
0.5%
2342 8
 
0.5%
3646 8
 
0.5%
5473 7
 
0.4%
7441 7
 
0.4%
5207 6
 
0.4%
6272 6
 
0.4%
Other values (885) 1601
95.5%
ValueCountFrequency (%)
1010 1
 
0.1%
1081 4
0.2%
1091 3
0.2%
1200 1
 
0.1%
1223 1
 
0.1%
1232 1
 
0.1%
1261 3
0.2%
1274 4
0.2%
1281 4
0.2%
1359 2
0.1%
ValueCountFrequency (%)
19973 1
 
0.1%
19943 2
0.1%
19859 3
0.2%
19847 3
0.2%
19665 1
 
0.1%
19658 1
 
0.1%
19636 1
 
0.1%
19627 1
 
0.1%
19626 2
0.1%
19613 1
 
0.1%

MonthlyRate
Real number (ℝ)

Distinct903
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14770.048
Minimum636
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:53.062583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum636
5-th percentile3481.2
Q18509
median15332
Q320990
95-th percentile25761
Maximum26999
Range26363
Interquartile range (IQR)12481

Descriptive statistics

Standard deviation7112.2039
Coefficient of variation (CV)0.48152882
Kurtosis-1.207622
Mean14770.048
Median Absolute Deviation (MAD)6236
Skewness-0.061418209
Sum24769371
Variance50583444
MonotonicityNot monotonic
2023-03-06T21:41:53.226326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4223 9
 
0.5%
8952 8
 
0.5%
15891 8
 
0.5%
22490 7
 
0.4%
16900 7
 
0.4%
9150 7
 
0.4%
11652 7
 
0.4%
20364 6
 
0.4%
26999 6
 
0.4%
20439 6
 
0.4%
Other values (893) 1606
95.8%
ValueCountFrequency (%)
636 1
 
0.1%
2125 2
0.1%
2137 1
 
0.1%
2253 2
0.1%
2323 2
0.1%
2326 2
0.1%
2338 3
0.2%
2354 3
0.2%
2373 3
0.2%
2396 3
0.2%
ValueCountFrequency (%)
26999 6
0.4%
26959 2
 
0.1%
26897 3
0.2%
26894 1
 
0.1%
26862 2
 
0.1%
26849 2
 
0.1%
26767 1
 
0.1%
26703 1
 
0.1%
26589 4
0.2%
26551 3
0.2%

PercentSalaryHike
Real number (ℝ)

Distinct15
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.903399
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:53.395055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q317
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4208011
Coefficient of variation (CV)0.2295316
Kurtosis-0.063513479
Mean14.903399
Median Absolute Deviation (MAD)2
Skewness0.91450964
Sum24993
Variance11.70188
MonotonicityNot monotonic
2023-03-06T21:41:53.522195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
13 267
15.9%
14 258
15.4%
11 237
14.1%
12 235
14.0%
15 124
7.4%
18 97
 
5.8%
17 92
 
5.5%
19 79
 
4.7%
16 74
 
4.4%
22 57
 
3.4%
Other values (5) 157
9.4%
ValueCountFrequency (%)
11 237
14.1%
12 235
14.0%
13 267
15.9%
14 258
15.4%
15 124
7.4%
16 74
 
4.4%
17 92
 
5.5%
18 97
 
5.8%
19 79
 
4.7%
20 55
 
3.3%
ValueCountFrequency (%)
25 10
 
0.6%
24 16
 
1.0%
23 22
 
1.3%
22 57
3.4%
21 54
3.2%
20 55
3.3%
19 79
4.7%
18 97
5.8%
17 92
5.5%
16 74
4.4%

TotalWorkingYears
Real number (ℝ)

Distinct41
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.7096
Minimum0
Maximum41
Zeros13
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:53.642562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median9
Q314
95-th percentile26
Maximum41
Range41
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.2551346
Coefficient of variation (CV)0.67744213
Kurtosis1.1838082
Mean10.7096
Median Absolute Deviation (MAD)4
Skewness1.1452338
Sum17960
Variance52.636978
MonotonicityNot monotonic
2023-03-06T21:41:53.752267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
10 254
15.1%
6 156
 
9.3%
8 116
 
6.9%
9 114
 
6.8%
7 104
 
6.2%
1 103
 
6.1%
5 94
 
5.6%
4 77
 
4.6%
12 54
 
3.2%
15 53
 
3.2%
Other values (31) 552
32.9%
ValueCountFrequency (%)
0 13
 
0.8%
1 103
6.1%
2 30
 
1.8%
3 48
 
2.9%
4 77
4.6%
5 94
5.6%
6 156
9.3%
7 104
6.2%
8 116
6.9%
9 114
6.8%
ValueCountFrequency (%)
41 1
 
0.1%
40 1
 
0.1%
38 2
 
0.1%
37 2
 
0.1%
36 2
 
0.1%
35 1
 
0.1%
34 4
0.2%
33 8
0.5%
32 2
 
0.1%
31 9
0.5%

YearsInCurrentRole
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1443053
Minimum0
Maximum18
Zeros307
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:53.842480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5833977
Coefficient of variation (CV)0.86465581
Kurtosis0.68077127
Mean4.1443053
Median Absolute Deviation (MAD)3
Skewness0.94266312
Sum6950
Variance12.840739
MonotonicityNot monotonic
2023-03-06T21:41:53.919264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 415
24.7%
0 307
18.3%
7 263
15.7%
3 166
 
9.9%
4 125
 
7.5%
8 103
 
6.1%
9 77
 
4.6%
1 43
 
2.6%
6 40
 
2.4%
5 34
 
2.0%
Other values (9) 104
 
6.2%
ValueCountFrequency (%)
0 307
18.3%
1 43
 
2.6%
2 415
24.7%
3 166
 
9.9%
4 125
 
7.5%
5 34
 
2.0%
6 40
 
2.4%
7 263
15.7%
8 103
 
6.1%
9 77
 
4.6%
ValueCountFrequency (%)
18 4
 
0.2%
17 6
 
0.4%
16 6
 
0.4%
15 5
 
0.3%
14 14
 
0.8%
13 14
 
0.8%
12 11
 
0.7%
11 16
 
1.0%
10 28
 
1.7%
9 77
4.6%

YearsWithCurrManager
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1866428
Minimum0
Maximum17
Zeros298
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-03-06T21:41:54.025631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5761892
Coefficient of variation (CV)0.85419019
Kurtosis0.16568537
Mean4.1866428
Median Absolute Deviation (MAD)3
Skewness0.8177646
Sum7021
Variance12.789129
MonotonicityNot monotonic
2023-03-06T21:41:54.102465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 387
23.1%
0 298
17.8%
7 265
15.8%
3 174
10.4%
8 126
 
7.5%
4 107
 
6.4%
1 71
 
4.2%
9 57
 
3.4%
5 37
 
2.2%
6 34
 
2.0%
Other values (8) 121
 
7.2%
ValueCountFrequency (%)
0 298
17.8%
1 71
 
4.2%
2 387
23.1%
3 174
10.4%
4 107
 
6.4%
5 37
 
2.2%
6 34
 
2.0%
7 265
15.8%
8 126
 
7.5%
9 57
 
3.4%
ValueCountFrequency (%)
17 8
 
0.5%
16 2
 
0.1%
15 6
 
0.4%
14 7
 
0.4%
13 17
 
1.0%
12 22
 
1.3%
11 30
 
1.8%
10 29
 
1.7%
9 57
3.4%
8 126
7.5%

Interactions

2023-03-06T21:41:34.956740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:48.379910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:54.380113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:00.879263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:06.825512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:13.409489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:19.327854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:25.736964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:31.970244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:38.285495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:43.547738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:49.911896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:57.507197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:05.635732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:12.273118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:19.149631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:25.339195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:29.891830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:35.288794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:48.751287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:54.717805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:01.218944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:07.168177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:13.741838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:19.667249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:26.060067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:32.322886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:38.517271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:43.873782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:50.277831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:57.818940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:06.093246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:12.489182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:19.533974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:25.546271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:30.204046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:35.585165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:49.069824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:55.081186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:01.564839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:07.526254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:14.062995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:20.134836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:26.378911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:32.670814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:38.959355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:44.211800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:50.619946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:58.235687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:06.552387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:12.692898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:19.923998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:25.765325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:30.506853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:35.890355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:49.411101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:55.471936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:01.892401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:07.846552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:14.358864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:20.483552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:26.990230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:33.032186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:39.130080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:44.539531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:50.974211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:58.692136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:07.034342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:13.061214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:20.291628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:25.975993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:30.792665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:36.200392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:49.669294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:55.823726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:02.294786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:08.217448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:14.703494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:20.859037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:27.326788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:33.394110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:39.249810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:45.274451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:51.364122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:59.214570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:07.512299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:13.497520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:20.664901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:26.164041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:31.051781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:36.479491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:49.911536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:56.176051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:02.686155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:08.568872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:15.015342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:21.180475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:27.637639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:33.721757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:39.364063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:45.576312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:51.817250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:59.684125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:07.947022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:13.875321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:21.041825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:26.381290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:31.298353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:36.824870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:50.302218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:56.527124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:03.043747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:08.924484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:15.342286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:21.504648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:28.025513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:34.104162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:39.470759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:45.915162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:52.348411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:00.184796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:08.455468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:14.276507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:21.422229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:26.703892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:31.541895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:37.060639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:50.538350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:56.829896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:03.371822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:09.277754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:15.655068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:21.792971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:28.322916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:34.433348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:39.677912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:46.240396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:52.813739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:00.618085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:08.868167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:14.682333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:21.762928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:26.959873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:31.802498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:37.404956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:50.935794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:57.163145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:03.730053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:09.648533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:16.015245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:22.118317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:28.678646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:34.790092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:40.028797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:46.573861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:53.280179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:01.114367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:09.161890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:15.076864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:22.156677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:27.259529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:32.043477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:37.722669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:51.282552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:57.516083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:04.067485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:09.991320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:16.342909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:22.480076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:29.011798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:35.153415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:40.391618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:46.891155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:53.686587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:01.581238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:09.419406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:15.528883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:22.543563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:27.510564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:32.343491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:38.021059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:51.610431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:57.825506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:04.379136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:10.308101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:16.657879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:22.816376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:29.355902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:35.476583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:40.723720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:47.208151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:54.062836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:02.030827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:09.655316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:15.945975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:22.913978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:27.741576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:32.619051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:38.912508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:51.968743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:58.176666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:04.737353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:10.671505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:17.008104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:23.198668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:29.695801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:35.841974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:41.108978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:47.549497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:54.491914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:02.530230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:09.950432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:16.289143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:23.318520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:28.049964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:32.940576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:39.188749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:52.324431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:58.768368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:05.089829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:11.317733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:17.341902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:23.571937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:30.022399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:36.213708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:41.475653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:47.893153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:54.913670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:03.017231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:10.285868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:16.700235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:23.685821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:28.336654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:33.220265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:39.540471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:52.677478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:59.163355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:05.360101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:11.666204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:17.676122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:23.933165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:30.363736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:36.562057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:41.838557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:48.226375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:55.386849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:03.487517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:10.560538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:17.042364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:23.966797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:28.583525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:33.488539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:39.856977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:53.008304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:59.485687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:05.624032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:12.008931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:17.992983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:24.259999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:30.671530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:36.912627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:42.194147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:48.563772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:55.818195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:03.907275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:10.892626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:17.426841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:24.256356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:28.876028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:33.727655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:40.170842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:53.346487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:59.823481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:05.914300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:12.364096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:18.325599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:24.630119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:31.013187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:37.269922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:42.534030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:48.897423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:56.156587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:04.349892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:11.213927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:17.873091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:24.541952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:29.120803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:34.010025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:40.477041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:53.663943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:00.159888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:06.189354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:12.708273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:18.618937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:25.025679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:31.305679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:37.595555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:42.846095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:49.210579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:56.522261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:04.739446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:11.453943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:18.276739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:24.829289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:29.349466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:34.321912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:40.797711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:39:54.032413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:00.517072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:06.466177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:13.066756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:18.978883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:25.375268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:31.642507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:37.983758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:43.198711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:49.587131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:40:57.055346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:05.209269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:12.031301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:18.675558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:25.130317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:29.608545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-06T21:41:34.640971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-03-06T21:41:54.222554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
EducationTrainingTimesLastYearDistanceFromHomeYearsSinceLastPromotionYearsAtCompanyCurrManagerTotalYearsInJobsNumCompaniesWorkedTotalSatisfactionAgeDailyRateHourlyRateMonthlyIncomeMonthlyRatePercentSalaryHikeTotalWorkingYearsYearsInCurrentRoleYearsWithCurrManagerEnvironmentSatisfactionStockOptionLevelJobInvolvementJobSatisfactionRelationshipSatisfactionWorkLifeBalance
Education1.0000.006-0.0160.0330.1210.0030.0780.102-0.0080.232-0.0060.0110.1240.026-0.0390.1880.0900.1040.0390.0320.0290.0000.0000.000
TrainingTimesLastYear0.0061.0000.006-0.023-0.063-0.051-0.0350.013-0.0290.032-0.0300.058-0.0160.026-0.010-0.030-0.069-0.0600.0140.0000.0000.0000.0000.000
DistanceFromHome-0.0160.0061.000-0.017-0.0140.0230.002-0.0210.003-0.0340.018-0.003-0.0520.0270.017-0.018-0.0100.0070.0160.0440.0000.0000.0000.000
YearsSinceLastPromotion0.033-0.023-0.0171.0000.5170.3100.354-0.0470.0760.1780.039-0.0430.2920.013-0.0910.3560.4970.4870.0000.0000.0210.0000.0480.000
YearsAtCompany0.121-0.063-0.0140.5171.0000.5490.706-0.1880.0170.2500.068-0.0810.4880.029-0.0720.5960.8670.8730.0160.0000.0450.0000.0320.000
CurrManagerTotal0.003-0.0510.0230.3100.5491.0000.396-0.3960.041-0.2170.014-0.0760.044-0.007-0.010-0.0230.5210.7480.0140.0220.0000.0090.0090.000
YearsInJobs0.078-0.0350.0020.3540.7060.3961.000-0.5350.0460.2040.068-0.0870.4290.019-0.0270.5520.6010.6150.0000.0140.0650.0000.0390.000
NumCompaniesWorked0.1020.013-0.021-0.047-0.188-0.396-0.5351.000-0.0370.407-0.0200.0690.160-0.017-0.0300.331-0.141-0.1450.0290.0000.0000.0230.0190.030
TotalSatisfaction-0.008-0.0290.0030.0760.0170.0410.046-0.0371.0000.044-0.031-0.0280.009-0.010-0.0220.0110.0050.0270.3090.0000.2530.3300.3330.059
Age0.2320.032-0.0340.1780.250-0.2170.2040.4070.0441.0000.0350.0320.4200.007-0.0530.6360.2150.1960.0470.0960.0000.0000.0220.000
DailyRate-0.006-0.0300.0180.0390.0680.0140.068-0.020-0.0310.0351.0000.0010.023-0.012-0.0110.0430.0590.0410.0000.0000.0620.0000.0130.000
HourlyRate0.0110.058-0.003-0.043-0.081-0.076-0.0870.069-0.0280.0320.0011.000-0.032-0.0190.021-0.031-0.079-0.0750.0310.0620.0130.0290.0460.040
MonthlyIncome0.124-0.016-0.0520.2920.4880.0440.4290.1600.0090.4200.023-0.0321.0000.044-0.1030.6540.4250.4140.0000.0280.0000.0570.0220.046
MonthlyRate0.0260.0260.0270.0130.029-0.0070.019-0.017-0.0100.007-0.012-0.0190.0441.0000.0490.0050.0350.0040.0490.0000.0000.0000.0300.023
PercentSalaryHike-0.039-0.0100.017-0.091-0.072-0.010-0.027-0.030-0.022-0.053-0.0110.021-0.1030.0491.000-0.063-0.046-0.0540.0000.0520.0000.0000.0200.040
TotalWorkingYears0.188-0.030-0.0180.3560.596-0.0230.5520.3310.0110.6360.043-0.0310.6540.005-0.0631.0000.5160.5300.0510.0500.0600.0000.0000.000
YearsInCurrentRole0.090-0.069-0.0100.4970.8670.5210.601-0.1410.0050.2150.059-0.0790.4250.035-0.0460.5161.0000.7810.0000.0360.0000.0000.0170.000
YearsWithCurrManager0.104-0.0600.0070.4870.8730.7480.615-0.1450.0270.1960.041-0.0750.4140.004-0.0540.5300.7811.0000.0000.0000.0250.0680.0000.000
EnvironmentSatisfaction0.0390.0140.0160.0000.0160.0140.0000.0290.3090.0470.0000.0310.0000.0490.0000.0510.0000.0001.0000.0190.0210.0230.0260.000
StockOptionLevel0.0320.0000.0440.0000.0000.0220.0140.0000.0000.0960.0000.0620.0280.0000.0520.0500.0360.0000.0191.0000.0260.0000.0150.031
JobInvolvement0.0290.0000.0000.0210.0450.0000.0650.0000.2530.0000.0620.0130.0000.0000.0000.0600.0000.0250.0210.0261.0000.0210.0000.025
JobSatisfaction0.0000.0000.0000.0000.0000.0090.0000.0230.3300.0000.0000.0290.0570.0000.0000.0000.0000.0680.0230.0000.0211.0000.0150.000
RelationshipSatisfaction0.0000.0000.0000.0480.0320.0090.0390.0190.3330.0220.0130.0460.0220.0300.0200.0000.0170.0000.0260.0150.0000.0151.0000.000
WorkLifeBalance0.0000.0000.0000.0000.0000.0000.0000.0300.0590.0000.0000.0400.0460.0230.0400.0000.0000.0000.0000.0310.0250.0000.0001.000

Missing values

2023-03-06T21:41:41.261919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-06T21:41:42.055014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EnvironmentSatisfactionStockOptionLevelEducationJobInvolvementJobSatisfactionRelationshipSatisfactionWorkLifeBalanceTrainingTimesLastYearDistanceFromHomeYearsSinceLastPromotionYearsAtCompanyCurrManagerTotalYearsInJobsNumCompaniesWorkedTotalSatisfactionAgeDailyRateHourlyRateMonthlyIncomeMonthlyRatePercentSalaryHikeTotalWorkingYearsYearsInCurrentRoleYearsWithCurrManager
041334232247100.80000010.0013.25365994225965099131008
1113314338040.7500004.0012.25359214628991077817423
23233443326130.5000004.0013.50327188046271649517422
3303313112060.1333335.0032.5038148840534713384141502
42043133054310.32258131.0012.2550101737190331980513311410
5303324302110.0000001.2053.0027566564197710311600
62043113210010.0000001.0011.75349443612811690013100
7413344232130.3333333.0023.754010097430671291612622
84131413364160.5000002.2582.50511297436439212211318149
9311333229790.8888899.0013.0025806822741795015978
EnvironmentSatisfactionStockOptionLevelEducationJobInvolvementJobSatisfactionRelationshipSatisfactionWorkLifeBalanceTrainingTimesLastYearDistanceFromHomeYearsSinceLastPromotionYearsAtCompanyCurrManagerTotalYearsInJobsNumCompaniesWorkedTotalSatisfactionAgeDailyRateHourlyRateMonthlyIncomeMonthlyRatePercentSalaryHikeTotalWorkingYearsYearsInCurrentRoleYearsWithCurrManager
1667402132351130.6666673.00000012.50282076622072620412322
1668211332209220.0869573.83333362.5044636911962722456132322
1669103244322220.3333330.66666792.75448486023722684919622
16701033433423010.0000005.75000042.754877694667312147132300
167113424233166100.5833334.00000032.255512767859937129171297
16724033324210100.80000010.00000013.003094573872214255191008
1673113324432140.3000002.50000042.5032130348354415972191023
16742032133224010.0000001.00000012.002911843628041532211100
16752224332390100.80000010.00000013.00364414854064051211038
16763033342320780.30000010.00000013.2536114135259317381191023